The author begins by pointing out that automation is not the same as learning (she sensibly groups AI, big data, and machine learning together under this umbrella) and that not all automation involves learning. This is particularly true when one considers decision-making. Some business decisions require learning but others can be automated without it. She suggests that automation without learning is appropriate when the problem is relatively straightforward. This is true, but our experience is that even quite complex decisions can sometimes be automated without learning. If the complexity comes from regulations and the need to be compliant, for instance, then a rules-based decision-making approach can automate the decision without any need for learning.

The author goes on to talk about learning technologies and their potential. Good machine learning problems, she says, involve a need for prediction rather than a causal understanding as well as a problem that is relatively insulated from outside influences.

The first element, a need for prediction rather than a causal understanding, is critical in any analytical solution. The set of technologies described by data science, analytics, machine learning, data mining and predictive analytics all drive towards predictions and probabilities not causal relationships. A good analytic model will tell you how likely something is to be true – is this claim likely to be fraudulent, how likely is this person to pay back a loan, how appealing is this offer to this person? Root cause analysis and causal linkages require different approaches.

The second element, a problem that is relatively insulated from outside influences, is trickier. What do you do if there are outside influences? She points out that machine learning and other analytic technologies cannot “access any knowledge outside of the data you provide.” If your business problem – the decision you are trying to automate – is not well insulated or has elements for which you don’t have good data, are you just out of luck? How can you apply these learning technologies to a problem that has too many other influences? How can you even tell what influences matter? How can you get enough clarity on the business problem to even make this assessment?

This is where decision modeling really shines. By helping you clearly describe the decision-making you need to automate or improve, decision modeling breaks down the problem into manageable pieces. Each piece can be assessed to see if learning would both help and be practical. Machine learning and other analytic techniques can be applied to these pieces while the overall decision model shows how these pieces are part of the whole. Compliance with regulations and other outside influences are clearly shown in the model too, making sure that these are not neglected in the rush to apply hot new technologies. The decision model also let’s you assess the error rate you can tolerate, something the author correctly identifies as a critical success factor in using these technologies.

Few decision-making problems can be solved only with machine learning technologies but many can benefit from the appropriate application of these analytic approaches. Decision modeling brings clarity to the business problem so you can see exactly how and where to apply them.